COVID-19 and human development: An approach for classification of HDI with deep CNN

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dc.contributor.authorKavuran, Gurkan
dc.contributor.authorGokhan, Seyma
dc.contributor.authorYeroglu, Celaleddin
dc.date.accessioned2026-06-19T06:40:52Z
dc.date.available2026-06-19T06:40:52Z
dc.date.issued2023
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThe measures taken during the pandemic have had lasting effects on people's lives and perceptions of the ability of national and multilateral institutions to drive human development. Policies that changed people's behavior were at the heart of containing the spread of the virus. As a result, it has become a systemic human development crisis affecting health, the economy, education, social life, and accumulated gains. This study shows how the relationship of the Human Development Index (HDI), which has combined effects on health, education, and the economy, should be considered in the context of pandemic factors. First, COVID-19 data of the countries received from a public and credible source were extracted and organized into an acceptable structure. Then, we applied statistical feature selection to determine which variables are closely related to HDI and enabled the Deep Con-volutional Neural Network (DCNN) model to give more accurate results. The Continuous Wavelet Transform (CWT) and scalogram methods were used for the time-series data visualization. Three different images of each country are combined into a single image to penetrate each other for ease of processing. These images were made suitable for the input of the ResNet-50 network, which is a pre-trained DCNN model, by going through various preprocessing processes. After the training and validation processes, the feature vectors in the fc1000 layer of the network were drawn and given to the Support Vector Machine Classifier (SVMC) input. We achieved total performance metrics of specificity (88.2%), sensitivity (96.5%), precision (99%), F1 Score (94.9%) and MCC (85.9%).
dc.description.sponsorshipInonu University Scientific Research Projects Management Unit [FYL-2021-2377]
dc.description.sponsorshipThis study was funded by Inonu University Scientific Research Projects Management Unit with the project number FYL-2021-2377.
dc.identifier.doi10.1016/j.bspc.2022.104499
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.orcid0000-0003-2651-5005
dc.identifier.orcid0000-0002-6106-2374
dc.identifier.pmid36530217
dc.identifier.scopus2-s2.0-85143866657
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.104499
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5937
dc.identifier.volume81
dc.identifier.wosWOS:000898625200009
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectHuman Development Index
dc.subjectDeep Learning
dc.subjectCovid-19
dc.subjectContinuous Wavelet Transform
dc.subjectArtificial Intelligence
dc.subjectClassification
dc.titleCOVID-19 and human development: An approach for classification of HDI with deep CNN
dc.typeArticle

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